Real-world systems ranging from airline routes to cryptocurrency transfers are naturally modelled as dynamic graphs whose topology changes over time. Conventional benchmarks judge dynamic-graph learners by a handful of task-specific scores, yet seldom ask whether the embeddings themselves remain a truthful, interpretable reflection of the evolving network. We formalize this requirement as representation integrity and derive a family of indexes that measure how closely embedding changes follow graph changes. Three synthetic scenarios, Gradual Merge, Abrupt Move, and Periodic Re-wiring, are used to screen forty-two candidate indexes. Based on which we recommend one index that passes all of our theoretical and empirical tests. In particular, this validated metric consistently ranks the provably stable UASE and IPP models highest. We then use this index to do a comparative study on representation integrity of common dynamic graph learning models. This study exposes the scenario-specific strengths of neural methods, and shows a strong positive rank correlation with one-step link-prediction AUC. The proposed integrity framework, therefore, offers a task-agnostic and interpretable evaluation tool for dynamic-graph representation quality, providing more explicit guidance for model selection and future architecture design.
Anton Ivashkevich, Matija Piškorec, Claudio J. Tessone
We describe a prototype of a fully capable Ethereum Proof-of-Work (PoW) blockchain network running on multiple Raspberry Pi (RPi) computers. The prototype is easy to set up and is intended to function as a completely standalone system, using a local WiFi router for connectivity. It features LCD screens for visualization of the local state of blockchain ledgers on each RPi, making it ideal for educational purposes and to demonstrate fundamental blockchain concepts to a wide audience. For example, a functioning PoW consensus is easily visible from the LCD screens, as well as consensus degradation which might arise from various factors, including peer-to-peer topology and communication latency - all parameters which can be configured from the central web-based interface.
Vision Transformers achieve strong performance across computer vision tasks but suffer from quadratic computational complexity with respect to token count, limiting deployment in resource-constrained environments. Existing token pruning methods rely on attention scores to identify important tokens, but attention mechanisms capture query-specific relevance rather than intrinsic information content, potentially discarding tokens that carry information for subsequent layers or different downstream tasks. We propose fractal-guided token pruning, a method that leverages the correlation dimension Dcorr of token embeddings as a task-agnostic measure of geometric complexity. Our key insight is that tokens with high Dcorr span higher-dimensional manifolds in representation space, indicating complex patterns, while tokens with low Dcorr collapse to simpler structures representing redundant information. By computing a local Dcorr for each token and pruning those with the lowest values, our method retains geometrically complex tokens independent of attention-based relevance. The correlation dimension quantifies how token embeddings fill the representation space: embeddings from uniform background regions cluster tightly in low-dimensional subspaces (low Dcorr), while embeddings from complex textures or object boundaries spread across higher-dimensional manifolds (high Dcorr), reflecting their richer information content. Experiments on CIFAR-10 and CIFAR-100 with fine-tuned ViT-B/16 models show that fractal-guided pruning consistently outperforms random and norm-based pruning across all tested ratios. At forty percent pruning, fractal pruning maintains 92.26% accuracy on CIFAR-10 with only a 0.99 percentage point drop from the 93.25% baseline while achieving 1.17× speedup. Our approach provides a geometry-based criterion for token importance that complements attention-based methods and shows promising generalization between CIFAR-10 and CIFAR-100 datasets.
Regional banks emerged around the 1960s with the mission of contributing to the development and integration of Latin America, primarily through the financing of infrastructure projects, essential to the region's industrialization and trade flows.In 2000, the South American Regional Integration Initiative (IIRSA) was created, under whose Secretariat the Inter-American Development Bank (IDB), the Development Bank of Latin America (CAF), and FONPLATA -Development Bank -began working together to promote territorial planning and find financing solutions.Even with the dissolution of IIRSA, resulting from the paralysis of the Union of South American Nations (UNASUR) starting in 2017, the coordinated action of the three banks continued, through initiatives such as the Alliance for the Integration and Development of Latin America and the Caribbean (ILAT), the Sucre Declaration, and the "South American Integration Routes," demonstrating the resilience of infrastructure integration in the face of political change.Therefore, the overall objective of this thesis was to analyze the contribution of the development banks IDB, CAF, and FONPLATA to building regional infrastructure integration in South America.The methodology involved identifying integration models and operational concepts under which these banks operate; identifying the specific problems of Latin American regional infrastructure and its financing; and analyzing the performance of IDB, CAF, and FONPLATA both individually, focusing on documents, projects, and institutional structures focused on integration, and collectively, from the emergence of IIRSA to the Integration Routes.It was found that, despite the current general crisis in Latin American regionalism, both intellectual and institutional, the three banks are at the center of building a governance system for financing regional infrastructure in South America.However, they have shifted from a model centralized in IIRSA to one, after the end of this Initiative, focused on decentralized cooperation.
The Nepali government has declared all cryptocurrency-related activities illegal due to its exclusive currency issuance authority and stringent foreign exchange regulations. This prohibition is based on robust anti-money laundering laws and the potential applicability of evolving digital legislation. The article also assesses the costs and risks associated with illicit cryptocurrency activities and encompassing severe legal consequences, financial exposure, and cyber security threats. Finally, it explores the paradox of Nepal Rastra Bank's exploration into a Central Bank Digital Currency (CBDC), suggesting a recognition of digital currency's future while prioritizing national control and stability. The study concludes by emphasizing the imperative for public adherence to existing prohibitions while acknowledging the long-term trajectory towards digital financial innovation.
The global decentralized identity market is undergoing exponential transformation as organizations worldwide shift toward secure, user-centric identity management frameworks. Valued at USD 1.52 billion in 2024, the market is projected to reach USD 56.83 billion by 2030, expanding at an extraordinary CAGR of 82.9% from 2025 to 2030. This manuscript provides an in-depth analysis of decentralized identity systems that utilize blockchain and distributed ledger technologies to eliminate central points of failure, enhance user privacy, and safeguard digital interactions. It further explores the major drivers influencing market growth, including rising identity fraud, rapid digitalization, proliferation of cloud platforms, and implementation of strict regulatory data protection standards. Despite high initial investments posing a challenge for small and medium enterprises, increasing integration of artificial intelligence and machine learning in decentralized identity frameworks offers promising future opportunities. The study concludes with key segmental insights, regional dynamics, competitive landscape, and future implications for the decentralized identity ecosystem.
Mehdi Talaie, Farkhondeh Jabari, Asghar Akbari Foroud
Blockchain technology as a new technology has been able to attract the attention of global communities. The applications of blockchain technology are developing rapidly due to its very desirable features, including decentralization, high data security, visibility, transparency and programmability. Among the applications of blockchain technology, we can mention the optimization of water management systems and sustainable development of water. Therefore, this chapter first examines the concept and structure of blockchain and its function. It also examines the components of blockchain technologies, including consensus protocols, smart contracts, data encryption and distributed ledgers. Then, the history of blockchain technology is presented and the classification of blockchain-based systems is discussed. After that, the strengths, weaknesses, opportunities and threats of blockchain technology are evaluated. Some applications of this technology will be introduced. Also, considering the importance of water and its sustainability, the role of blockchain technology for the water industry and intelligent water management will be investigated.
Simon Fernandez-Vazquez, Omar Alexander León García, Julián Ramírez, Salvatore Cannella
Blockchain is currently a key focus in both academic and industrial domains. The pivotal next step for the sustainable growth of blockchain technology lies in its widespread adoption. This article delineates the research subjects, constraints, gaps, and emerging trends in blockchain within the hydrosystem sector. Our examination, based on 136 publications from the Web of Science (WoS), underscores a thorough exploration of issues, particularly emphasizing security, privacy, and latency. However, the suggested remedies for these issues are still in early stages of development. This study serves as a useful starting point for researchers, offering insights and recommendations for future research areas in the application of the distributed ledger within the hydrosystem sector.
The competitive hospitality sector faces a growing credibility crisis, where rising consumer skepticism regarding "greenwashing" severely limits the ability of hotels to capture the Sustainable Revenue Premium. This research addresses a critical gap in Sustainable Supply Chain Management (SSCM) literature by empirically modeling the "Credibility Mechanism"—the process by which digital technology resolves information asymmetry to monetize sustainability claims. Focusing on the complex Food and Beverage (F&B) supply chains of emerging archipelagic economies, the study employs a rigorous sequential mixed-methods design. First, Design Science Research was utilized to architect a permissioned cross-chain blockchain framework integrating Zero-Knowledge Proofs (ZKPs) for verifiable, private provenance. Subsequently, Partial Least Squares-Structural Equation Modeling (PLS-SEM) confirmed that blockchain-enabled transparency significantly mitigates perceived greenwashing risk, which in turn fosters Customer Trust. Critically, the study validates financial outcomes using a Stochastic Frontier Bayesian Model (SFBM) applied to longitudinal hotel data. Results demonstrate that adopting this traceable framework yields an 8.4% increase in F&B revenue efficiency and sustains a 5.1% price premium for ethically sourced items. These findings provide profound theoretical advancements by redefining SCM risk mitigation through Information Governance rather than material redundancy. Managerially, the research offers a data-driven justification for high-tech investment, proving that verifiable transparency is a direct revenue driver essential for competitive advantage in opaque markets.
The integration of renewable energy sources (RES) and distributed energy resources (DER) into local energy markets is transforming modern power grids toward a decentralized architecture. To enhance the efficiency of decentralized energy trading, blockchain technology has been widely adopted in constructing peer-to-peer energy trading platforms, providing incentives for renewable energy generation and utilization. However, the rapid growth of small-scale suppliers and intermittent DERs introduces significant challenges to grid stability, including supply–demand imbalances and voltage fluctuations. To address these challenges, we propose a blockchain-based energy trading system architecture designed to enable a self-regulating, sustainable, and resilient grid. The proposed system architecture achieves grid stability through three key components: (i) precise endpoint control via AI Agents with lightweight forecasting models integrated into existing hardware systems, (ii) flexible distributed control through an efficient incentive mechanism, named Proof of Prediction, based on a blockchain-based automated trading process, and (iii) macro-level coordination via global regulation roles. We implemented a prototype of the proposed architecture on the Ethereum Blockchain and applied it to a microgrid-scale distributed automated trading environment. Our evaluation results show that using the architecture we proposed achieves a peak-shaving rate of up to 29.6%, while maintaining the overall supply–demand deviation of around 5% on average, demonstrating its strong potential as a foundation for building stable and modern power grids.
Vehicle platoon (VP), as a typical form of traffic cooperation, can significantly enhance traffic efficiency and safety in Vehicular Ad hoc Networks (VANETs). However, malicious vehicles in VP poses a severe threat to the security of entire VP, requiring to be efficiently traced by identity authentication. In this paper, we propose a lattice-based efficient and traceable privacy-preserving batch authentication scheme for vehicle platoon in VANETs, named LETA. First, we design a dynamic VP identity structure VPD-Tree which is constructed based on hash tree and pseudonyms of vehicles to preserve privacy. Then, an aggregate signature is constructed based on VPD-tree and modular lattice for secure and efficient batch authentication of VP. Finally, Zero-Knowledge Proofs (ZKP) is applied on the VPD-Tree structure to anonymously and efficiently trace the malicious vehicles of VP. Security analysis shows that LETA achieves stronger security guarantees, thereby offering a more secure solution than existing approaches. Moreover, performance evaluations show that LETA achieves lower computation and communication overheads through the VPD-tree structure and efficient batch authentication scheme.
Smart contracts enable programmatic agreements but face two persistent problems: high on-chain cost (throughput/latency) and weak privacy (public ledger exposes transaction semantics). We propose a hybrid on-chain/off-chain commitment scheme (HOC-C) that combines lightweight on-chain commitments, verifiable off-chain computation, and succinct zero-knowledge proofs to deliver privacy-preserving contract execution at scale. In HOC-C, sensitive inputs and heavy computations are executed off-chain by a consortium of replicated verifiers; the verifiers publish a succinct zk-SNARK proof of correct execution plus a small state commitment on-chain. The on-chain contract verifies the proof and updates state atomically. To prevent malicious collusion among verifiers, HOC-C integrates an economic incentive layer and challenge windows where anyone can publish refutation proofs; the refutation burden is designed to be less than the honest-verifier cost. We implement HOC-C using a prototype that plugs into an EVM-compatible chain (Ethereum testnet) and evaluate performance for representative workloads (private auctions, confidential supply-chain workflows, private token-transfer batching). The system reduces gas cost by an order of magnitude compared to naive on-chain execution while preserving end-to-end confidentiality for user inputs. We analyze security properties (soundness, liveness, and economic incentive compatibility) and discuss trade-offs: proof generation latency vs. throughput, verifier decentralization vs. amortized cost. HOC-C offers a practical roadmap for adopting private, inexpensive smart contracts on mainstream blockchains.
Hikaru Okamoto, Vu Trung Duong Le, Hoai Luan Pham, Van Tinh Nguyen · 5 authors
Zero-Knowledge Proof (ZKP) is a privacy-preserving protocol that allows a prover to demonstrate the validity of a statement without revealing its details. A widely used primitive of ZKP, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK), has attracted significant attention in edge computing; however, edge devices face severe resource constraints when processing its computational bottleneck, Montgomery multiplication. This paper proposes MMzk, a lightweight hardware architecture optimized for 384-bit Coarsely Integrated Operand Scanning (CIOS) Montgomery multiplication, which is primarily employed in multi-scalar multiplication (MSM) of zk-SNARKs. To achieve both high performance and resource efficiency, MMzk core operating at the System-on-Chip (SoC) level integrates three key optimizations: resource sharing of two adders, a subtractor, and a multiplier; effective memory organization for large-data processing; and double-buffering memory scheduling. Implementation results on a Xilinx ZCU102 SoC show that MMzk core operates at up to 250MHz with a utilization of 3,590 LUTs, 1,648 FFs, 16 DSPs, and 6 BRAM36 blocks. Compared with existing FPGA-based counterparts, it achieves a throughput of 657.53Mbps (up to 35.9 times higher) and an area efficiency of 81.48Kbps/eLUT (up to 9.5 times higher). Furthermore, real-time evaluation demonstrates an energy efficiency of 9,030Mbps/W at 250MHz and 67mW, surpassing modern CPUs by up to 52.9 times. These results confirm MMzk core as an energy-efficient and high-performance solution for zk-SNARK-based blockchain systems and other Montgomery multiplication applications.
Marko Štaka, Sonja Ristić, Miroslav Stefanović, Danilo Nikolić · 5 authors
Blockchain technology and smart contracts are central to decentralized applications. Operating on blockchain introduces challenges in design, development, and execution costs. On Ethereum, gas fees make optimization essential. This paper analyzes common mistakes affecting gas consumption and outlines strategies to improve efficiency. Using literature review and error systematization, key issues are identified and practical steps proposed. These recommendations, grounded in scientific research, aim to help developers design optimized and cost-effective smart contracts.
Thanh Hai To, Vu Trung Duong Le, Van Tinh Nguyen, Van-Tuan Luu · 6 authors
Zero-knowledge proofs have become an essential component for providing privacy and verifiability in decentralized systems. Existing techniques, such as zk-SNARKs, have intrinsic constraints, including the necessity for a trusted setup and sensitivity to quantum attacks, which make them unsuitable for high-assurance applications such as digital banking. In this paper, we provide a viable zk-STARK-based verification system that eliminates the trusted setup while maintaining long-term post-quantum security. Our system integrates off-chain proof generation using Cairo 0, on-chain verification through Cairo 2 smart contracts on Starknet, and decentralized proof storage via IPFS and Filecoin. Experimental results show that the time users wait from transaction submission to confirmation is approximately 1.15 seconds on average, with Cairo 2 contract verification completing in 0.73 seconds and consuming a gas cost of 0.0158 STRK (equivalent to approximately 0.00229 USD) per first-time execution. End-to-end latency for proof publication to IPFS and Filecoin is evaluated separately, reaching up to 63 minutes and 41 hours, respectively. However, these steps run asynchronously without impacting user responsiveness. Compared to zk-SNARK and Bulletproof-based systems, our hashbased, transparent architecture is more scalable, auditable, and quantum-resistant. These findings show that it is possible to install real-world, privacy-preserving, post-quantum verification pipelines for next-generation financial systems.
Transitioning to renewable energy is thus a very important component of global efforts toward combating climate change, especially in emerging economies where energy demand is fast outpacing supply. Carbon markets have emerged as a vital financial mechanism for supporting renewable energy projects by enabling the trade of carbon credits. The following abstract discusses how carbon markets affect multi-dimensionally the financial flows of renewable energy in developing nations: attracting investment, reducing capital costs, driving technology innovation, and delivering decentralized energy. Through case studies from Kenya, India, and Brazil, the article illustrates how carbon markets have indeed served to mobilize such large-scale renewable projects as wind farms and solar installations that improve the lot of rural and underserved communities. Despite the promise of carbon markets, it still faces regulatory gaps, market volatility, high transaction costs, and limited participation from local stakeholders. This may spell out actionable solutions, such as the development of regional carbon trading systems, enhancement of voluntary carbon markets, blended finance models, and the integration of emerging economies into global carbon market initiatives within frameworks like those under the Paris Agreement. Carbon markets could have a real catalyzing role in the transition toward renewable energy, with accelerated rates of greenhouse gas emission reduction and sustainable development in emerging economies, if they are able to successfully address these tacked barriers.
Mohammad Aftab Mahmud, Md.Ridwan Mahmud, Md. Zakir Hossan, Asif Mahmud · 6 authors
Bangladesh is one of the nations highly vulnerable to climate impacts. It lacks a structured and transparent voluntary carbon market to support the country’s sustainability goals. This paper presents a decentralized blockchain platform designed to enable carbon credit issuance, verification, and trading in accordance with Bangladesh’s pledge to reduce carbon emissions. Using Ethereum smart contracts and MetaMask wallet integration, the system ensures secure and transparent transactions. A Minimum Viable Product (MVP) prototype was developed and tested, demonstrating operations including land registration, carbon credit calculation, and peer-to-peer trading of tokenized credits. The platform features a Next.js frontend, Solidity-based smart contracts, and a registry module to ensure traceable credit ownership. The system combats common challenges in traditional carbon markets, including opacity and fraud risk. Functional evaluations showed reliable performance across use cases, with low gas costs and positive user experience during wallet-based interactions. This work provides a solution for climate finance infrastructure in developing nations. It lays the foundation for regulatory integration, decentralized data storage via InterPlanetary File System (IPFS), and future collaboration with global carbon exchanges.
To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing.
Riku Miyake, Toru NAKANISHI, Teruaki Kitasuka, Zhuotao Lian
Although current digital identity systems are centralized, decentralized systems based on Verifiable Credentials (VCs) are gaining attention and moving towards practical implementation. As one of VC systems, a VC system with selective disclosure has been proposed, where credentials are represented as directed graph based on the concept of Linked Data (LD). However, in the existing VC system, the verification time increases depending on the number of RDF terms that correspond to vertices and edges in the proved graph, due to the characteristics of the utilized signature scheme. Meanwhile, a zero-knowledge proof system for directed graphs using a pairing-based accumulator has been proposed. This system is characterized by its verification time and proof data size being independent of the number of vertices and edges in the graph. In this paper, we propose a LD-based VC system with selective disclosure that leverages the zero-knowledge proof system on graph; the verification time and proof size are independent of the number of vertices and edges. Furthermore, we reduce the proof data size by modifying the signature scheme from AHO signatures to SPS-EQ signatures and from the pairing-based accumulator to a set commitment. We implement and evaluate the proposed system on a PC.
Vera Mita Nia, Hermanto Siregar, Roy Sembel, Nimmi Zulbainarni
This study explores how surprise shocks in Indonesia’s macroeconomic environment—specifically interest rates, inflation, and exchange rates—affect the returns and volatility of key financial assets, including gold, Bitcoin (BTC), stocks (JKSE), and government bonds. Utilizing the EGARCH(1,1) model, this research demonstrates that gold exhibits enduring resilience as a safe-haven during periods of rising inflation and interest rate fluctuations. In contrast, Bitcoin is marked by pronounced speculative dynamics, showing persistent, asymmetric, and extreme volatility, yet delivering attractive gains when market conditions are strong. The findings indicate that stocks and bonds are particularly susceptible to changes in macroeconomic variables, thereby illustrating the vulnerabilities typical of emerging markets. Through portfolio optimization employing the Mean-Variance approach, gold dominates the optimal asset allocation, while Bitcoin provides notable diversification benefits. The results of backtesting using the Kupiec and Basel Traffic Light procedures confirm that GARCH-family risk estimations are robust and meet international regulatory standards. Furthermore, analysis of the Sharpe ratio and cumulative returns reveals that Mean-Variance portfolios consistently outperform equally weighted alternatives by delivering higher risk-adjusted returns and lower overall volatility. By integrating advanced econometric methods with real-world macroeconomic shocks in an Indonesian context, this research offers practical insights for both investors and policymakers addressing asset allocation under uncertainty, while laying the groundwork for future work involving broader asset universes and sophisticated modeling techniques.
تناولت هذه الدراسة الفقهية مسألة المتاجرة بالرموز غير القابلة للاستبدال (NFTs)، وهي رموز رقمية فريدة تُسجَّل على تقنية البلوك تشين وتُستخدم لإثبات ملكية الأصول الرقمية. وهدفت الدراسة إلى بيان الحكم الفقهي لهذه المعاملات في ضوء القواعد العامة للمعاملات المالية في الشريعة الإسلامية، من خلال تحليل خصائص هذه الرموز ومجالات استخدامها، وبيان مدى انطباق الضوابط الشرعية على تلك المعاملات. وقد خلصت الدراسة إلى أن الحكم يتوقف على طبيعة كل حالة، حيث إن بعض صور المتاجرة بهذه الرموز قد تندرج تحت البيوع الجائزة، إذا توفرت فيها شروط الصحة وانتفت عنها المحاذير الشرعية؛ بينما بعض الصور الأخرى قد تُعد من المعاملات المحرمة، بسبب الغرر أو الجهالة أو المقامرة. وأوصت الدراسة بضرورة وضع أطر شرعية واضحة لتنظيم هذه المعاملات في ظل التطورات الرقمية المتسارعة. This jurisprudential study explores the issue of trading in Non-Fungible Tokens (NFTs), which are unique digital assets registered on blockchain technology and used to prove ownership of digital content. The study aims to determine the Islamic legal ruling on such transactions in light of the general principles of financial dealings in Islamic law, by analyzing the features of NFTs, their uses, and the extent to which they comply with Shariah standards. The study concludes that the ruling depends on the nature of each case. Some forms of NFT trading may be considered permissible sales if the necessary conditions are met and no Shariah violations are involved. However, other forms may be deemed prohibited due to uncertainty, ambiguity, or elements of gambling.
Globally, the construction industry is faced with several challenges like inefficiencies, disputes, and a lack of transparency. This paper uses the case of Lusaka, Zambia to investigate the impact of adopting emerging digital technologies in the construction industry, with a particular focus on smart contracts and blockchain technology. Drawing on existing literature and theoretical frameworks, Technology Acceptance Model (TAM), this paper argues that through the adoption of smart contracts and Blockchain technologies, the construction industry in Zambia and the world over could result in many benefits. Lusaka was an ideal case study for validating these hypothesized benefits. The findings of this research identified both benefits and challenges to the adoption of smart contracts and blockchain technologies. The identified benefits include the efficiency in construction processes, an improvement in the supply chain management, mitigation of risks, and a fostering of greater trust among stakeholders within the construction industry. Emerging from the research data were challenges relating to technological illiteracy, absence of regulatory frameworks, and high costs of initial investment. The paper concludes by emphasizing that the benefits surpass the challenges hence the need for Zambia and other similar developing economies to consider transforming the construction industry processes through adopting blockchain technologies and smart contracts to revolutionizing construction practices.
In the modern context of information technology development, the management of labor processes in complex IT projects acquires the features of self-organization and dynamic adaptation. The article examines the principles of configuring agent interactions within the labor environment of IT projects as a tool for enhancing the efficiency of team management. The agent-based interaction model makes it possible to consider each team member as an autonomous agent capable of making decisions, adapting behavior to the task context, and interacting with other elements of the system within a distributed environment. Conceptual foundations have been developed for constructing the architecture of agent interactions, based on the principles of cognitive exchange, communicative coherence, flexible role distribution, and multilevel task management. It is determined that the key factor in the effectiveness of such interactions is the balance between agent autonomy and centralized process coordination. A systematic classification of agent configuration types is proposed: hierarchical, decentralized, hybrid, and cognitively adaptive, which differ in the level of information connectivity and the system’s response speed. The study also investigates the impact of cognitive factors on the dynamics of interactions between agents, such as trust, intellectual compatibility, role specialization, and the ability for collective learning. A model for assessing the effectiveness of agent interaction is proposed, using indicators of performance, informational transparency, decision synchronization level, and team adaptability index. It is established that the configuration of agent connections directly determines the speed of decision-making, the coherence of actions, and the level of project innovation activity. The results of the study have practical significance for building multi-agent IT team management systems, developing algorithms for adaptive resource allocation, and creating cognitive project management dashboards. The proposed principles can be used to optimize communication processes, reduce the risk of conflicts, and enhance the resilience of organizational structures under conditions of high labor environment complexity.
Open access
Information Systems and Technology Applications
Mathematical Control Systems and Analysis
Technology and Human Factors in Education and Health